Automatic Measurement of Fish Weight and Size by Processing Underwater Hatchery Images

摘要

Retrieving information from fish hatcheries is a key need in the Colombian fishing industry because it allows hatchery managers to determine necessary food amounts and measure other population parameters. However, traditional measurement methods involve extracting live fish from the ponds. This results in stress and the possibility of injury. Researchers have proposed automated measuring systems for shortening measurement times and reducing fish stress, but they must fulfill several prerequisites before they can retrieve fish information. These include mounting underwater camera systems and applying image enhancement and segmentation algorithms. In this paper, the literature revolving around these issues is reviewed and a novel approach is proposed. It is shown that using a single camera for image acquisition in a controlled setup is appropriate because it enables better management of sample size and image acquisition conditions. Furthermore, a combination of homomorphic filtering, contrast limited adaptive histogram equalization (CLAHE) and guided filtering for fish image enhancement was used. The fish were then segmented using a combination of 2D saliency detection and morphological operators. Finally, fish length was obtained using a third-degree polynomial regression on the fish mid-points. The length was calculated to estimate the weight with several regression algorithms. This approach was shown to be the most appropriate method for regression of fish weight based on length.